journal article·2023-05-30Open access

Correlation of Bone Textural Parameters with Age in the Context of Orthopedic X-ray Studies

Paweł Kamiński, Rafał Obuchowicz, Aleksandra Stępień, Julia Lasek, Elżbieta Pociask, Adam Piórkowski

Applied Sciences· Volume 13· Issue 11

Abstract

The aim of this study was to establish a relationship between the textural parameters observed in X-ray images of bones and the age of the individual. The study utilized a meticulous visual analysis of the images to identify significant correlations between textural features and age. Five distinct regions of interest, namely the Wing of the Ilium, Neck of the Femur, Greater Trochanter, Ischium, and Shaft of the Femur, were identified on both sides of the body. Textural parameters were then measured for each of these regions. The left femoral neck showed the most noteworthy associations, with the textures generated from the histogram of oriented gradients and gray-level co-occurrence matrix exhibiting the strongest correlations (ρ −0.52, p-value 4.95 × 10−14). The main finding of the current study is that correlation of age-dependent bone structure differences in the femoral neck area is higher than in other structures of the femur. This proposed methodology has the potential to aid in the early detection of osteoporosis, which is crucial for devising treatment plans and identifying potential risks associated with bone fragility.
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Research context

OpenAlex topics: Bone health and osteoporosis research · Radiomics and Machine Learning in Medical Imaging · Bone and Joint Diseases

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Clinic of Locomotor Disorders, Andrzej Frycz Modrzewski Krakow University, 30-705 Krakow, Poland

Małopolska Orthopedic and Rehabilitation Hospital, Modrzewiowa 22, 30-224 Krakow, Poland

Department of Diagnostic Imaging, Jagiellonian University Medical College, Kopernika 19, 31-501 Krakow, Poland

Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland

Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland

References (68)

Show source-provided references
1. Bone Biology and the Clinical Implications for Osteoporosis10.1093/ptj/86.1.77 ↗
2. Aging Diminishes Lamellar and Woven Bone Formation Induced by Tibial Compression in Adult C57BL/610.1016/j.bone.2014.05.006 ↗
3. Bone Remodeling10.1196/annals.1365.035 ↗
4. Gartner, L.P., and Hiatt, J.L. (2011). Concise Histology, Elsevier.
5. Mechanical Properties and the Hierarchical Structure of Bone10.1016/S1350-4533(98)00007-1 ↗
6. McNally, E.A., Schwarcz, H.P., Botton, G.A., and Arsenault, A.L. (2012). A Model for the Ultrastructure of Bone Based on Electron Microscopy of Ion-Milled Sections. PLoS ONE, 7.10.1371/journal.pone.0029258 ↗
7. X-ray-Based Medical Imaging and Resolution10.2214/AJR.14.13126 ↗
8. The Amazing Osteocyte10.1002/jbmr.320 ↗
9. Osteonal and Hemi-Osteonal Remodeling: The Spatial and Temporal Framework for Signal Traffic in Adult Human Bone10.1002/jcb.240550303 ↗
10. The Skeleton as an Endocrine Organ10.1038/nrrheum.2012.157 ↗
11. Noirrit-Esclassan, E., Valera, M.-C., Tremollieres, F., Arnal, J.-F., Lenfant, F., Fontaine, C., and Vinel, A. (2021). Critical Role of Estrogens on Bone Homeostasis in Both Male and Female: From Physiology to Medical Implications. Int. J. Mol. Sci., 22.10.3390/ijms22041568 ↗
13. Biology of Bone Tissue: Structure, Function, and Factors That Influence Bone Cells
14. Exercise Effects on Bone Mass in Postmenopausal Women Are Site-Specific and Load-Dependent10.1002/jbmr.5650110211 ↗
15. Peak Bone Mass10.1007/BF01623429 ↗
16. Vitamin D Deficiency and Secondary Hyperparathyroidism in the Elderly: Consequences for Bone Loss and Fractures and Therapeutic Implications10.1210/edrv.22.4.0437 ↗
17. Estrogen and the Skeleton10.1016/j.tem.2012.03.008 ↗
18. MaZda—The software package for textural analysis of biomedical images10.1007/978-3-642-04462-5_8 ↗
19. Pociask, E., Nurzynska, K., Obuchowicz, R., Bałon, P., Uryga, D., Strzelecki, M., Izworski, A., and Piórkowski, A. (2021). Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs. Sensors, 21.10.3390/s21227481 ↗
20. An Analysis of Variance Test for Normality (Complete Samples)10.1093/biomet/52.3-4.591 ↗
21. Textural Features for Image Classification10.1109/TSMC.1973.4309314 ↗
22. Image Classification Using RapidEye Data: Integration of Spectral and Textual Features in a Random Forest Classifier10.1109/JSTARS.2017.2774807 ↗
23. Ultrasound GLCM Texture Analysis of Radiation-Induced Parotid-Gland Injury in Head-and-Neck Cancer Radiotherapy: An in Vivo Study of Late Toxicity: Ultrasound Assessment of Post-RT Parotid Gland10.1118/1.4747526 ↗
24. Experiments in the Visual Perception of Texture10.1038/scientificamerican0475-34 ↗
25. The Current Role of Radiography in the Assessment of Skeletal Tumors and Tumor-like Lesions10.1016/S0720-048X(98)00047-3 ↗
26. Trabecular Bone Analysis in CT and X-ray Images of the Proximal Femur for the Assessment of Local Bone Quality10.1109/TMI.2009.2020734 ↗
27. Aging of Microstructural Compartments in Human Compact Bone10.1359/jbmr.2003.18.6.1012 ↗
28. Bone Strength and Its Determinants10.1007/s00198-002-1345-4 ↗
29. Age-Related Changes in the Biochemical Properties of Human Cancellous Bone Collagen: Relationship to Bone Strength10.1007/s002239900683 ↗
30. Bone Health During the Menopause Transition and Beyond10.1016/j.ogc.2018.07.012 ↗
31. Aging Increases Stromal/Osteoblastic Cell-Induced Osteoclastogenesis and Alters the Osteoclast Precursor Pool in the Mouse10.1359/JBMR.050503 ↗
32. Age-Related Bone Loss: Old Bone, New Facts10.1159/000048929 ↗
33. Mechanical Basis of Bone Strength: Influence of Bone Material, Bone Structure and Muscle Action
34. The Heterogeneity in Femoral Neck Structure and Strength10.1002/jbmr.1827 ↗
35. Low Bone Mass and Fast Rate of Bone Loss at Menopause: Equal Risk Factors for Future Fracture: A 15-Year Follow-up Study10.1016/8756-3282(96)00102-0 ↗
36. Soldati, E., Roseren, F., Guenoun, D., Mancini, L., Catelli, E., Prati, S., Sciutto, G., Vicente, J., Iotti, S., and Bendahan, D. (2022). Multiscale Femoral Neck Imaging and Multimodal Trabeculae Quality Characterization in an Osteoporotic Bone Sample. Materials, 15.10.3390/ma15228048 ↗
37. Effects of Age and Sex on the Strength and Cortical Thickness of the Femoral Neck10.1016/j.bone.2010.12.004 ↗
38. Skeletal Effects of Estrogen and Mechanical Loading Are Structurally Distinct10.1016/j.bone.2008.06.005 ↗
39. Computerised Analysis of Osteoporotic Bone Patterns Using Texture Parameters Characterising Bone Architecture10.1259/bjr.20101115 ↗
40. Texture Analysis in Quantitative MR Imaging. Tissue Characterisation of Normal Brain and Intracranial Tumours at 1.5 T10.1177/028418519503600204 ↗
41. Texture Anisotropy of the Brain’s White Matter as Revealed by Anatomical MRI10.1109/TMI.2007.895481 ↗
42. Mahmoud-Ghoneim, D., Alkaabi, M.K., de Certaines, J.D., and Goettsche, F.-M. (2008). The Impact of Image Dynamic Range on Texture Classification of Brain White Matter. BMC Med. Imaging, 8.10.1186/1471-2342-8-18 ↗
43. Characterization of Breast Cancer Types by Texture Analysis of Magnetic Resonance Images10.1016/j.acra.2009.08.012 ↗
44. Comparison of Automated and Visual Texture Analysis in MRI: Characterization of Normal and Diseased Skeletal Muscle10.1016/S0730-725X(99)00066-1 ↗
45. Bone Texture Analysis for Prediction of Incident Radiographic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) Study10.1016/j.joca.2019.02.796 ↗
46. The Influence of Bit-Depth Reduction on Correlation of Texture Features with a Patient’s Age
47. The Influence of Age on Morphometric and Textural Vertebrae Features in Lateral Cervical Spine Radiographs10.1007/978-3-030-49666-1_6 ↗
48. Association of Bone Mineral Density with Bone Texture Attributes Extracted Using Routine Magnetic Resonance Imaging10.6061/clinics/2020/e1766 ↗
49. The Measurement of Bone Quality Using Gray Level Co-Occurrence Matrix Textural Features10.1166/jmihi.2016.1812 ↗
50. Radiology Report Generation for Proximal Femur Fractures Using Deep Classification and Language Generation Models10.1016/j.artmed.2022.102281 ↗
51. Meena, T., and Roy, S. (2022). Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift. Diagnostics, 12.10.3390/diagnostics12102420 ↗
52. Detecting Intertrochanteric Hip Fractures with Orthopedist-Level Accuracy Using a Deep Convolutional Neural Network10.1007/s00256-018-3016-3 ↗
53. Detection and Classification of Mandibular Fracture on CT Scan Using Deep Convolutional Neural Network10.1007/s00784-022-04427-8 ↗
54. Gender Prediction with the Parameters Obtained from Pelvis Computed Tomography Images and Machine Learning Algorithms10.4103/jasi.jasi_280_20 ↗
55. Zhou, J., Li, Z., Zhi, W., Liang, B., Moses, D., and Dawes, L. (December, January 29). Using Convolutional Neural Networks and Transfer Learning for Bone Age Classification. Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia.10.1109/DICTA.2017.8227503 ↗
56. Segmentation of Bone Structure in X-ray Images Using Convolutional Neural Network10.4316/AECE.2013.01015 ↗
57. Fully Automated Pelvic Bone Segmentation in Multiparameteric MRI Using a 3D Convolutional Neural Network10.1186/s13244-021-01044-z ↗
58. Deep Learning for Classification of Bone Lesions on Routine MRI10.1016/j.ebiom.2021.103402 ↗
59. Deep Learning-Based Classification of Primary Bone Tumors on Radiographs: A Preliminary Study10.1016/j.ebiom.2020.103121 ↗
60. Kozakiewicz, M. (2022). Measures of Corticalization. J. Clin. Med., 11.10.3390/jcm11185463 ↗
61. Obuchowicz, R., Nurzynska, K., Pierzchala, M., Piorkowski, A., and Strzelecki, M. (2023). Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys. J. Clin. Med., 12.10.3390/jcm12082762 ↗
62. Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmenta-tion and 3D Texture Analysis of Routine Abdominal CT10.3389/fendo.2021.792760 ↗
63. Clinical interest of bone texture analysis in osteoporosis: A case control multicenter study10.1007/s00198-007-0532-8 ↗
64. Zheng, K., and Makrogiannis, S. (2016, January 16–20). Bone texture characterization for osteoporosis diagnosis using digital radiography. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.10.1109/EMBC.2016.7590879 ↗
65. Mandibular Trabecular Bone Analysis Using Local Binary Pattern for Osteoporosis Diagnosis10.31661/jbpe.v9i1Feb.743 ↗
66. Using texture analysis of head CT images to differentiate osteoporosis from normal bone density10.1016/j.ejrad.2019.05.009 ↗
67. Khider, M., Taleb-Ahmed, A., Dubois, P., and Haddad, B. (2007, January 22–26). Classification of trabecular bone texture from MRI and CT scan images by multi resolution analysis. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.10.1109/IEMBS.2007.4353613 ↗
68. Castellanos, N.P., Martínez, E., and Gutierrez, J. (September, January 30). Improving osteoporosis diagnosis in children using image texture analysis. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.10.1109/IEMBS.2011.6091527 ↗